Learning from demonstration using MDP induced metrics

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6322 LNAI, Issue PART 2, 2010, Pages 385-401

  Melo, Francisco S ^a   Lopes, Manuel ^b  

^a INSTITUTO SUPERIOR TÉCNICO   (Portugal)

^b UNIVERSITY OF PLYMOUTH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COSTS; GENERALIZATION PERFORMANCE; INVERSE REINFORCEMENT LEARNING; KERNEL BASED APPROACH; LEARNING FROM DEMONSTRATION; OPTIMAL POLICIES; STATE-SPACE; SUPERVISED LEARNING METHODS;

LEARNING SYSTEMS;

DEMONSTRATIONS;

EID: 78049399307     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15883-4_25     Document Type: Conference Paper

References (29)

1. Abbeel, P.: Apprenticeship learning and reinforcement learning with application to robotic control. Ph. D. thesis, Dep. Computer Science, Stanford Univ (2008)
2. Abbeel, P., Ng, A.: Apprenticeship learning via inverse reinforcement learning. In: Proc. 21st Int. Conf. Machine Learning, pp. 1-8 (2004)
3. Argall, B., Chernova, S., Veloso, M.: A survey of robot learning from demonstration. Robotics and Autonomous Systems 57(5), 469-483 (2009)
4. Chernova, S., Veloso, M.: Interactive policy learning through confidence-based autonomy. J. Artificial Intelligence Research 34, 1-25 (2009)
5. Fern, A., Yoon, S., Givan, R.: Approximate policy iteration with a policy language bias. In: Adv. Neural Information Proc. Systems 16 (2003)
6. Fern, A., Yoon, S., Givan, R.: Approximate policy iteration with a policy language bias: Solving relational Markov decision processes. J. Artificial Intelligence Research 25, 75-118 (2006)
7. Ferns, N., Panangaden, P., Precup, D.: Metrics for finite Markov decision processes. In: Proc. 20th Conf. Uncertainty in Artificial Intelligence, pp. 162-169 (2004)
8. Ferns, N., Panangaden, P., Precup, D.: Metrics for Markov decision processes with infinite state-spaces. In: Proc. 21st Conf. Uncertainty in Artificial Intelligence, pp. 201-208 (2005)
9. Givan, R., Dean, T., Greig, M.: Equivalence notions and model minimization in Markov Decision Processes. Artificial Intelligence 147, 163-223 (2003)
10. Lagoudakis, M., Parr, R.: Reinforcement learning as classification: Leveraging modern classifiers. In: Proc. 20th Int. Conf. Machine Learning, pp. D424-D431 (2003)
11. Langford, J., Zadrozny, B.: Relating reinforcement learning performance to classification performance. In: Proc. 22nd Int. Conf. Machine Learning, pp. D473-D480 (2005)
12. Lazaric, A., Ghavamzadeh, M., Munos, R.: Analysis of a classification-based policy iteration algorithm. In: Proc. 27th Int. Conf. Machine Learning (to appear, 2010)
13. Lopes, M., Melo, F., Montesano, L., Santos-Victor, J.: Abstraction levels for robotic imitation: Overview and computational approaches. In: From Motor Learning to Interaction Learning in Robots, pp. 313-355 (2010)
14. Montesano, L., Lopes, M.: Learning grasping affordances from local visual descriptors. In: Proc. 8th Int. Conf. Development and Learning, pp. 1-6 (2009)
15. Neu, G., Szepesvári, C.: Apprenticeship learning using inverse reinforcement learning and gradient methods. In: Proc. 23rd Conf. Uncertainty in Artificial Intelligence, pp. 295-302 (2007)
16. Neu, G., Szepesvári, C.: Training parsers by inverse reinforcement learning. Machine Learning (2009) (accepted)
17. Ng, A., Russel, S.: Algorithms for inverse reinforcement learning. In: Proc. 17th Int. Conf. Machine Learning, pp. 663-670 (2000)
18. Pomerleau, D.: Efficient training of artificial neural networks for autonomous navigation. Neural Computation 3(1), 88-97 (1991)
19. Ramachandran, D., Amir, E.: Bayesian inverse reinforcement learning. In: Proc. 20th Int. Joint Conf. Artificial Intelligence, pp. 2586-2591 (2007)
20. Ratliff, N., Bagnell, J., Zinkevich, M.: Maximum margin planning. In: Proc. 23rd Int. Conf. Machine Learning, pp. 729-736 (2006)
21. Ravindran, B., Barto, A.: Approximate homomorphisms: A framework for nonexact minimization in Markov decision processes. In: Proc. 5th Int. Conf. Knowledge-Based Computer Systems (2004)
22. Saunders, J., Nehaniv, C., Dautenhahn, K.: Teaching robots by moulding behavior and scaffolding the environment. In: Proc. 1st Annual Conf. Human-Robot Interaction (2006)
23. Schölkopf, B., Smola, A.: Learning with kernels: Support vector machines, regularization, optimization and beyond. MIT Press, Cambridge (2002)
24. Settles, B.: Active learning literature survey. Tech. Rep. CS Tech. Rep. 1648, Univ. Wisconsin-Maddison (2009)
25. Syed, U., Schapire, R.: A game-theoretic approach to apprenticeship learning. In: Adv. Neural Information Proc. Systems, vol. 20, pp. 1449-1456 (2008)
26. Syed, U., Schapire, R., Bowling, M.: Apprenticeship learning using linear programming. In: Proc. 25th Int. Conf. Machine Learning, pp. 1032-1039 (2008)
27. Taylor, J., Precup, D., Panangaden, P.: Bounding performance loss in approximate MDP homomorphisms. In: Adv. Neural Information Proc. Systems, pp. 1649-1656 (2008)
28. Zhu, J., Hastie, T.: Kernel logistic regression and the import vector machine. In: Adv. Neural Information Proc. Systems. pp. 1081-1088 (2002)
29. Ziebart, B., Maas, A., Bagnell, J., Dey, A.: Maximum entropy inverse reinforcement learning. In: Proc. 23rd AAAI Conf. Artificial Intelligence, pp. 1433-1438 (2008)